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Journal articles on the topic 'Human keypoint detection'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

Ahmad, Niaz, Jawad Khan, Jeremy Yuhyun Kim, and Youngmoon Lee. "Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 69–76. http://dx.doi.org/10.1609/aaai.v36i1.19880.

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Despite the advances in multi-person pose estimation, state-of-the-art techniques only deliver the human pose structure.Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat

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Mundt, Marion, Zachery Born, Molly Goldacre, and Jacqueline Alderson. "Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose." Sensors 23, no. 1 (2022): 78. http://dx.doi.org/10.3390/s23010078.

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The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source p

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Li, Zhen, Yuliang Gao, Qingqing Hong, Yuren Du, Seiichi Serikawa, and Lifeng Zhang. "Keypoint3D: Keypoint-Based and Anchor-Free 3D Object Detection for Autonomous Driving with Monocular Vision." Remote Sensing 15, no. 5 (2023): 1210. http://dx.doi.org/10.3390/rs15051210.

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Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection serves as a breakthrough for providing precise and dependable 3D geometric information. Inspired by practical driving experiences of human experts, a pure visual scheme takes sufficient responsibility for safe and stable autonomous driving. In this paper, we proposed an anchor-free and keypoi

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Zhang, Jing, Zhe Chen, and Dacheng Tao. "Towards High Performance Human Keypoint Detection." International Journal of Computer Vision 129, no. 9 (2021): 2639–62. http://dx.doi.org/10.1007/s11263-021-01482-8.

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Gajic, Dusan, Gorana Gojic, Dinu Dragan, and Veljko Petrovic. "Comparative evaluation of keypoint detectors for 3d digital avatar reconstruction." Facta universitatis - series: Electronics and Energetics 33, no. 3 (2020): 379–94. http://dx.doi.org/10.2298/fuee2003379g.

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Three-dimensional personalized human avatars have been successfully utilized in shopping, entertainment, education, and health applications. However, it is still a challenging task to obtain both a complete and highly detailed avatar automatically. One approach is to use general-purpose, photogrammetry-based algorithms on a series of overlapping images of the person. We argue that the quality of avatar reconstruction can be increased by modifying parts of the photogrammetry-based algorithm pipeline to be more specifically tailored to the human body shape. In this context, we perform an extensi

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Ferres, Kim, Timo Schloesser, and Peter A. Gloor. "Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut." Future Internet 14, no. 4 (2022): 97. http://dx.doi.org/10.3390/fi14040097.

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This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states “Anger”, “Fear”, “Happiness” and “Relaxation”. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly traine

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Aidoo, Evans, Xun Wang, Zhenguang Liu, et al. "Cofopose: Conditional 2D Pose Estimation with Transformers." Sensors 22, no. 18 (2022): 6821. http://dx.doi.org/10.3390/s22186821.

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Human pose estimation has long been a fundamental problem in computer vision and artificial intelligence. Prominent among the 2D human pose estimation (HPE) methods are the regression-based approaches, which have been proven to achieve excellent results. However, the ground-truth labels are usually inherently ambiguous in challenging cases such as motion blur, occlusions, and truncation, leading to poor performance measurement and lower levels of accuracy. In this paper, we propose Cofopose, which is a two-stage approach consisting of a person and keypoint detection transformers for 2D human p

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Wang, Xinran, Guoliang Li, and Feng Liu. "HRDS: A High-Dimensional Lightweight Keypoint Detection Network Enhancing HRNet with Dim-Channel and Space Gate Attention Using Kolmogorov-Arnold Networks." Electronics 14, no. 10 (2025): 2038. https://doi.org/10.3390/electronics14102038.

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Animal keypoint detection holds significant applications in fields such as biological behavior research and animal health monitoring. Although related research has reached a relatively mature stage of human keypoint detection, it still faces numerous challenges in the realm of animal keypoint detection. Firstly, there is a scarcity of keypoint detection datasets related to animals in public datasets. Secondly, existing solutions have adopted large-scale deep learning models to achieve higher accuracy, but these models are costly and difficult to widely promote within the industry. On the other

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Zwölfer, Michael, Martin Mössner, Helge Rhodin, Werner Nachbauer, and Dieter Heinrich. "Integration of a skier-specific keypoint detection model in a hybrid 3D motion capture pipeline." Current Issues in Sport Science (CISS) 9, no. 4 (2024): 013. http://dx.doi.org/10.36950/2024.4ciss013.

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Introduction & Purpose Alpine skiing, like many outdoor sports, presents significant challenges for motion capture due to its large capture volumes, high athlete speeds, variable environmental conditions, and occlusions, e.g., due to snow spray. While traditional marker-based motion capture systems offer highest precision in the lab, they are usually unsuitable for outdoor settings. Sensor-based methods, such as inertial measurement units, however, may suffer from inaccuracies due to sensor noise and drift, while they only provide relative segment positions (Fasel et al., 2018). Therefore,

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Nguyen, Hung-Cuong, Thi-Hao Nguyen, Jakub Nowak, Aleksander Byrski, Agnieszka Siwocha, and Van-Hung Le. "Combined YOLOv5 and HRNet for High Accuracy 2D Keypoint and Human Pose Estimation." Journal of Artificial Intelligence and Soft Computing Research 12, no. 4 (2022): 281–98. http://dx.doi.org/10.2478/jaiscr-2022-0019.

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Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of

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Lu, Shufang, Funan Lu, Xufeng Shou, and Shuaiyin Zhu. "DeepProfile: Accurate Under-the-Clothes Body Profile Estimation." Applied Sciences 12, no. 4 (2022): 2220. http://dx.doi.org/10.3390/app12042220.

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Accurate human body profiles have many potential applications. Image-based human body profile estimation can be regarded as a fine-grained semantic segmentation problem, which is typically used to locate objects and boundaries in images. However, existing image segmentation methods, such as human parsing, require significant amounts of annotation and their datasets consider clothes as part of the human body profile. Therefore, the results they generate are not accurate when the human subject is dressed in loose-fitting clothing. In this paper, we created and labeled an under-the-clothes human

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Lyu, Xinwei, Xinjia Li, Yuexin Zhang, and Wenlian Lu. "Two-Stage Method for Clothing Feature Detection." Big Data and Cognitive Computing 8, no. 4 (2024): 35. http://dx.doi.org/10.3390/bdcc8040035.

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The rapid expansion of e-commerce, particularly in the clothing sector, has led to a significant demand for an effective clothing industry. This study presents a novel two-stage image recognition method. Our approach distinctively combines human keypoint detection, object detection, and classification methods into a two-stage structure. Initially, we utilize open-source libraries, namely OpenPose and Dlib, for accurate human keypoint detection, followed by a custom cropping logic for extracting body part boxes. In the second stage, we employ a blend of Harris Corner, Canny Edge, and skin pixel

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Fu, Dengyu, and Wei Wu. "High-Resolution Representation Learning for Human Pose Estimation based on Transformer." Journal of Physics: Conference Series 2189, no. 1 (2022): 012023. http://dx.doi.org/10.1088/1742-6596/2189/1/012023.

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Abstract Human pose estimation requires accurate coordinate values for the prediction of human joints, which requires a high-resolution representation to effectively improve accuracy. For some difficult joint prediction tasks, it is not only necessary to look at the characteristics of the joint points themselves, but also to make judgments in combination with the context of the whole image. Generally, the resolution will be reduced when the context information is obtained. In this process, it will inevitably lose some spatial information and make the prediction inaccurate. In this paper, we pr

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Zhang, Jianqiang, Jing Hou, Qiusheng He, Zhengwei Yuan, and Hao Xue. "MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba." Sensors 24, no. 24 (2024): 8158. https://doi.org/10.3390/s24248158.

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Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the mod

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Jeong, Jeongseok, Byeongjun Park, and Kyoungro Yoon. "3D Human Skeleton Keypoint Detection Using RGB and Depth Image." Transactions of The Korean Institute of Electrical Engineers 70, no. 9 (2021): 1354–61. http://dx.doi.org/10.5370/kiee.2021.70.9.1354.

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Favorskaya, Margarita N., and Dmitriy N. Natalenko. "Semantically-Based Animal Pose Estimation in the Wild." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W5-2024 (December 16, 2024): 33–40. https://doi.org/10.5194/isprs-archives-xlviii-2-w5-2024-33-2024.

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Abstract. Accurate animal pose estimation in the wild is potentially useful for many downstream applications such as wildlife conservation. Currently, the main approach to assessing animal poses is based on identifying keypoints of the body and constructing the skeleton. However, a direct application of frameworks to human pose estimation is not successful due to the features of the skeletal structure of humans and mammals. In this study, we propose a two-stage method: coarse-tuning with animal detection using a bounding box, as is done in most similar methods, and fine-tuning with semantic se

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Meng, Xianjia, Yong Yang, Kang Li, and Zuobin Ying. "A Structure-Aware Adversarial Framework with the Keypoint Biorientation Field for Multiperson Pose Estimation." Wireless Communications and Mobile Computing 2022 (February 14, 2022): 1–17. http://dx.doi.org/10.1155/2022/3447827.

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Human pose estimation is aimed at locating the anatomical parts or keypoints of the human body and is regarded as a core component in obtaining detailed human understanding in images or videos. However, the occlusion and overlap upon human bodies and complex backgrounds often result in implausible pose predictions. To address the problem, we propose a structure-aware adversarial framework, which combines cues of local joint interconnectivity and priors about the holistic structure of human bodies, achieving high-quality results for multiperson human pose estimation. Effective learning of such

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Yuan, Ye, Jiahao Li, Qi Yu, et al. "A Two-Stage Facial Kinematic Control Strategy for Humanoid Robots Based on Keyframe Detection and Keypoint Cubic Spline Interpolation." Mathematics 12, no. 20 (2024): 3278. http://dx.doi.org/10.3390/math12203278.

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A plentiful number of facial expressions is the basis of natural human–robot interaction for high-fidelity humanoid robots. The facial expression imitation of humanoid robots involves the transmission of human facial expression data to servos situated within the robot’s head. These data drive the servos to manipulate the skin, thereby enabling the robot to exhibit various facial expressions. However, since the mechanical transmission rate cannot keep up with the data processing rate, humanoid robots often suffer from jitters in the imitation. We conducted a thorough analysis of the transmitted

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Li, Jun, and Xiangqing Dong. "Intelligent Surveillance of Airport Apron: Detection and Location of Abnormal Behavior in Typical Non-Cooperative Human Objects." Applied Sciences 14, no. 14 (2024): 6182. http://dx.doi.org/10.3390/app14146182.

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Most airport surface surveillance systems focus on monitoring and commanding cooperative objects (vehicles) while neglecting the location and detection of non-cooperative objects (humans). Abnormal behavior by non-cooperative objects poses a potential threat to airport security. This study collects surveillance video data from civil aviation airports in several regions of China, and a non-cooperative abnormal behavior localization and detection framework (NC-ABLD) is established. As the focus of this paper, the proposed framework seamlessly integrates a multi-scale non-cooperative object local

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Wenyue Liu. "STSP-Net: A Spatial-Temporal Skeletal Perception Network for Robust 3D Pose Estimation in Children's Sports." Journal of Information Systems Engineering and Management 10, no. 29s (2025): 975–86. https://doi.org/10.52783/jisem.v10i29s.4613.

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Introduction: Children's sports motion pose estimation has significant applications in sports training, health monitoring, and rehabilitation assessment. However, existing 3D pose estimation methods still face challenges in sports scenarios, including insufficient stability in keypoint detection, unreasonable 3D structures, and a lack of temporal consistency in motion trajectories. These issues lead to poor robustness in pose prediction under high-speed motion and occlusion conditions.Objectives: To address the limitations of current 3D pose estimation methods, this paper aims to propose a nov

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Ni, Peng, Dan Xiang, Dawei Jiang, Jianwei Sun, and Jingxiang Cui. "Federated Learning for Human Pose Estimation on Non-IID Data via Gradient Coordination." Sensors 25, no. 14 (2025): 4372. https://doi.org/10.3390/s25144372.

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Human pose estimation is an important downstream task in computer vision, with significant applications in action recognition and virtual reality. However, data collected in a decentralized manner often exhibit non-independent and identically distributed (non-IID) characteristics, and traditional federated learning aggregation strategies can lead to gradient conflicts that impair model convergence and accuracy. To address this, we propose the Federated Gradient Harmonization aggregation strategy (FedGH), which coordinates update directions by measuring client gradient discrepancies and integra

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Ling, Xufeng, Yu Zhu, Wei Liu, Jingxin Liang, and Jie Yang. "The Generation of Articulatory Animations Based on Keypoint Detection and Motion Transfer Combined with Image Style Transfer." Computers 12, no. 8 (2023): 150. http://dx.doi.org/10.3390/computers12080150.

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Knowing the correct positioning of the tongue and mouth for pronunciation is crucial for learning English pronunciation correctly. Articulatory animation is an effective way to address the above task and helpful to English learners. However, articulatory animations are all traditionally hand-drawn. Different situations require varying animation styles, so a comprehensive redraw of all the articulatory animations is necessary. To address this issue, we developed a method for the automatic generation of articulatory animations using a deep learning system. Our method leverages an automatic keypo

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Wang, Jue, and Zhigang Luo. "Pointless Pose: Part Affinity Field-Based 3D Pose Estimation without Detecting Keypoints." Electronics 10, no. 8 (2021): 929. http://dx.doi.org/10.3390/electronics10080929.

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Human pose estimation finds its application in an extremely wide domain and is therefore never pointless. We propose in this paper a new approach that, unlike any prior one that we are aware of, bypasses the 2D keypoint detection step based on which the 3D pose is estimated, and is thus pointless. Our motivation is rather straightforward: 2D keypoint detection is vulnerable to occlusions and out-of-image absences, in which case the 2D errors propagate to 3D recovery and deteriorate the results. To this end, we resort to explicitly estimating the human body regions of interest (ROI) and their 3

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Xu, Ruinian, Fu-Jen Chu, Chao Tang, Weiyu Liu, and Patricio Vela. "An Affordance Keypoint Detection Network for Robot Manipulation." IEEE Robotics and Automation Letters 6, no. 2 (2021): 2870–77. http://dx.doi.org/10.1109/lra.2021.3062560.

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Lu, Kai, and Dugki Min. "AiPE: A Novel Transformer-Based Pose Estimation Method." Electronics 13, no. 5 (2024): 967. http://dx.doi.org/10.3390/electronics13050967.

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Human pose estimation is an important problem in computer vision because it is the foundation for many advanced semantic tasks and downstream applications. Although some convolutional neural network-based pose estimation methods have achieved good results, these networks are still limited for restricted receptive fields and weak robustness, leading to poor detection performance in scenarios with blur or low resolution. Additionally, their highly parallelized strategy is likely to cause significant computational demands, requiring high computing power. In comparison to the convolutional neural

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Melo, César, Sandra Dixe, Jaime C. Fonseca, António H. J. Moreira, and João Borges. "AI Based Monitoring of Different Risk Levels in COVID-19 Context." Sensors 22, no. 1 (2021): 298. http://dx.doi.org/10.3390/s22010298.

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COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body tem

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Pansare, Dr (Mrs ). J. R. "A Dual-Mode Sign Language Recognition System for Education and Communication Using CNN and CNN-LSTM Architectures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49773.

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Abstract: The Diversified Sign Language Recognition System is designed to enhance educational accessibility for differently-abled individuals by providing an interactive platform to learn and practice sign language. Built using Human-Computer Interaction (HCI) principles, the system features a user-friendly interface with two main modes: Learning Mode and Understanding Mode. The Learning Mode uses a pre-trained CNN model on image-based datasets to help users learn static hand signs for alphabets in an engaging way. The Understanding Mode enables real-time recognition of dynamic gestures using

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Damarsiwi, Dyah Kartika, Elindra Ambar Pambudi, Maulida Ayu Fitriani, and Feri Wibowo. "Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods." Sinkron 8, no. 2 (2024): 852–60. http://dx.doi.org/10.33395/sinkron.v8i2.13556.

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Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The S

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Tinchev, Georgi, Adrian Penate-Sanchez, and Maurice Fallon. "SKD: Keypoint Detection for Point Clouds Using Saliency Estimation." IEEE Robotics and Automation Letters 6, no. 2 (2021): 3785–92. http://dx.doi.org/10.1109/lra.2021.3065224.

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Jin, Zechen, Yida Zheng, Jun Liu, and Yang Yu. "A Semantic Web-Based Approach for Bat Trajectory Reconstruction With Human Keypoint Information." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/ijswis.338999.

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Restoring the trajectory of a bat from a table tennis match video is critical in analyzing a table tennis technique and conducting statistical analysis. However, directly bat location detection in each frame is challenging due to changing shapes caused by varying movement directions and speeds, leading to ambiguity. This paper develops a novel two-stage method. The first stage utilizes YOLO for bat detection in each frame, followed by filtering out erroneous candidate boxes. In the second stage, the authors use a temporal prediction model that integrating human keypoint information and interpo

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Adolf, Jindrich, Jaromir Dolezal, Patrik Kutilek, Jan Hejda, and Lenka Lhotska. "Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset." Applied Sciences 12, no. 2 (2022): 799. http://dx.doi.org/10.3390/app12020799.

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In recent years, several systems have been developed to capture human motion in real-time using common RGB cameras. This approach has great potential to become widespread among the general public as it allows the remote evaluation of exercise at no additional cost. The concept of using these systems in rehabilitation in the home environment has been discussed, but no work has addressed the practical problem of detecting basic body parts under different sensing conditions on a large scale. In this study, we evaluate the ability of the OpenPose pose estimation algorithm to perform keypoint detec

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Liu, Qi, and Lichen Shi. "A pointer meter reading method based on human-like reading sequence and keypoint detection." Measurement 248 (May 2025): 116994. https://doi.org/10.1016/j.measurement.2025.116994.

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Qin, Xiayang, Jingxing Cao, Yonghong Zhang, Tiantian Dong, and Haixiao Cao. "Development of an Optimized YOLO-PP-Based Cherry Tomato Detection System for Autonomous Precision Harvesting." Processes 13, no. 2 (2025): 353. https://doi.org/10.3390/pr13020353.

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An accurate and efficient detection method for harvesting is crucial for the development of automated harvesting robots in short-cycle, high-yield facility tomato cultivation environments. This study focuses on cherry tomatoes, which grow in clusters, and addresses the complexity and reduced detection speed associated with the current multi-step processes that combine target detection with segmentation and traditional image processing for clustered fruits. We propose YOLO-Picking Point (YOLO-PP), an improved cherry tomato picking point detection network designed to efficiently and accurately i

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Sun, Yin-Tung Albert, Hsin-Chang Lin, Po-Yen Wu, and Jung-Tang Huang. "Learning by Watching via Keypoint Extraction and Imitation Learning." Machines 10, no. 11 (2022): 1049. http://dx.doi.org/10.3390/machines10111049.

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In recent years, the use of reinforcement learning and imitation learning to complete robot control tasks have become more popular. Demonstration and learning by experts have always been the goal of researchers. However, the lack of action data has been a significant limitation to learning by human demonstration. We propose an architecture based on a new 3D keypoint tracking model and generative adversarial imitation learning to learn from expert demonstrations. We used 3D keypoint tracking to make up for the lack of action data in simple images and then used image-to-image conversion to conve

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Luo, Zhongzhen, Wenjie Xue, Julia Chae, and Guoyi Fu. "SKP: Semantic 3D Keypoint Detection for Category-Level Robotic Manipulation." IEEE Robotics and Automation Letters 7, no. 2 (2022): 5437–44. http://dx.doi.org/10.1109/lra.2022.3157438.

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Tu, Haiyan, Zhengkun Qiu, Kang Yang, Xiaoyue Tan, and Xiujuan Zheng. "HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method." Applied Sciences 15, no. 6 (2025): 3025. https://doi.org/10.3390/app15063025.

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Human Pose Estimation (HPE) plays a critical role in medical applications, particularly within nursing robotics for patient monitoring. Despite its importance, HPE faces several challenges, including high rates of false positives and negatives, stringent real-time requirements, and limited computational resources, especially in complex backgrounds. In response, we introduce the HP-YOLO model, developed using the YOLOv8 framework, to effectively address these issues. We designed an Enhanced Large Separated Kernel Attention (ELSKA) mechanism and integrated it into the backbone network, thereby i

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Wang, Li, Bo Su, Qunpo Liu, Ruxin Gao, Jianjun Zhang, and Guodong Wang. "Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion." Electronics 12, no. 17 (2023): 3702. http://dx.doi.org/10.3390/electronics12173702.

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Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction

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Yokota, Masae, Soichiro Majima, Yushin Mochizuki, Sarthak Pathak, and Kazunori Umeda. "Home Appliance Operation via 3D Keypoint Based Gesture Detection in Body-Relative Command Spaces." International Journal of Automation Technology 19, no. 3 (2025): 216–25. https://doi.org/10.20965/ijat.2025.p0216.

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In this paper, we propose a flexible device control method using personalized command spaces that function as buttons on a virtual remote control that follows the user. By performing two different gestures in each space, the users can control various devices in a room. This system is implemented through multiple cameras and 3D human keypoint tracking. We experimentally evaluated the influence of command spaces arrangement on gesture recognition and determined the recognition accuracies for different gestures in each command space. The system demonstrated high usability, with even inexperienced

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Nan, Xiaohu, and Lei Ding. "Learning Geometric Feature Embedding with Transformers for Image Matching." Sensors 22, no. 24 (2022): 9882. http://dx.doi.org/10.3390/s22249882.

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Local feature matching is a part of many large vision tasks. Local feature matching usually consists of three parts: feature detection, description, and matching. The matching task usually serves a downstream task, such as camera pose estimation, so geometric information is crucial for the matching task. We propose the geometric feature embedding matching method (GFM) for local feature matching. We propose the adaptive keypoint geometric embedding module dynamic adjust keypoint position information and the orientation geometric embedding displayed modeling of geometric information about rotati

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Sarvesh Kumar. "Precision-Driven Real-Time Pose Estimation for Therapeutic Interventions: Advanced Heatmap Regression, Reference Video Alignment, and Real-Time Corrective Feedback." Journal of Information Systems Engineering and Management 10, no. 34s (2025): 299–310. https://doi.org/10.52783/jisem.v10i34s.5802.

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Accurate movement and posture are essential for effective physical therapy, as improper form can hinder recovery and worsen injuries. This project introduces a real-time human pose estimation system specifically designed for physical therapy, providing precise feedback on body alignment. Utilizing a mod- ified YOLOv8 architecture with custom heatmap regression, the system monitors key joints—particularly the wrist, elbow, and shoulder—vital for upper-body rehabilitation. Initially trained on a combined MPII and COCO 2017 dataset, the model was fine-tuned on a custom dataset of 6,000 images der

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Sun, Xuefei, Mohammed Jajere Adamu, Ruifeng Zhang, Xin Guan, and Qiang Li. "Pixel-Coordinate-Induced Human Pose High-Precision Estimation Method." Electronics 12, no. 7 (2023): 1648. http://dx.doi.org/10.3390/electronics12071648.

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Accurately estimating human pose is crucial for providing feedback during exercises or musical performances, but the complex and flexible nature of human joints makes it challenging. Additionally, traditional methods often neglect pixel coordinates, which are naturally present in high-resolution images of the human body. To address this issue, we propose a novel human pose estimation method that directly incorporates pixel coordinates. Our method adds a coordinate channel to the convolution process and embeds pixel coordinates into the feature map, while also using coordinate attention to capt

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Ye, Run Zhou, Arun Subramanian, Daniel Diedrich, Heidi Lindroth, Brian Pickering, and Vitaly Herasevich. "Effects of Image Quality on the Accuracy Human Pose Estimation and Detection of Eye Lid Opening/Closing Using Openpose and DLib." Journal of Imaging 8, no. 12 (2022): 330. http://dx.doi.org/10.3390/jimaging8120330.

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Objective: The application of computer models in continuous patient activity monitoring using video cameras is complicated by the capture of images of varying qualities due to poor lighting conditions and lower image resolutions. Insufficient literature has assessed the effects of image resolution, color depth, noise level, and low light on the inference of eye opening and closing and body landmarks from digital images. Method: This study systematically assessed the effects of varying image resolutions (from 100 × 100 pixels to 20 × 20 pixels at an interval of 10 pixels), lighting conditions (

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Jiang, Yi, Kexin Yang, Jinlin Zhu, and Li Qin. "YOLO-Rlepose: Improved YOLO Based on Swin Transformer and Rle-Oks Loss for Multi-Person Pose Estimation." Electronics 13, no. 3 (2024): 563. http://dx.doi.org/10.3390/electronics13030563.

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In recent years, there has been significant progress in human pose estimation, fueled by the widespread adoption of deep convolutional neural networks. However, despite these advancements, multi-person 2D pose estimation still remains highly challenging due to factors such as occlusion, noise, and non-rigid body movements. Currently, most multi-person pose estimation approaches handle joint localization and association separately. This study proposes a direct regression-based method to estimate the 2D human pose from a single image. The authors name this network YOLO-Rlepose. Compared to tradi

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Apurupa, Leela, J. D.Dorathi Jayaseeli, and D. Malathi. "An Integrated Technique for Image Forgery Detection using Block and Keypoint Based Feature Techniques." International Journal of Engineering & Technology 7, no. 3.12 (2018): 505. http://dx.doi.org/10.14419/ijet.v7i3.12.16168.

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The invention of the net has introduced the unthinkable growth and developments within the illustrious analysis fields like drugs, satellite imaging, image process, security, biometrics, and genetic science. The algorithms enforced within the twenty first century has created the human life more leisurely and secure, however the protection to the first documents belongs to the genuine person is remained as involved within the digital image process domain. a replacement study is planned during this analysis paper to discover. The key plan in the deliberate take a look at and therefore the detect

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Wu, Chengyu, Xin Wei, Shaohua Li, and Ao Zhan. "MSTPose: Learning-Enriched Visual Information with Multi-Scale Transformers for Human Pose Estimation." Electronics 12, no. 15 (2023): 3244. http://dx.doi.org/10.3390/electronics12153244.

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Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through

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Pandurevic, Dominik, Paweł Draga, Alexander Sutor, and Klaus Hochradel. "Analysis of Competition and Training Videos of Speed Climbing Athletes Using Feature and Human Body Keypoint Detection Algorithms." Sensors 22, no. 6 (2022): 2251. http://dx.doi.org/10.3390/s22062251.

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Compared to 25 years ago, the climbing sport itself has changed dramatically. From a rock climbing modification to a separation in three independent disciplines, the requirements to athletes and trainers increased rapidly. To ensure continuous improvement of the sport itself, the usage of measurement and sensor technology is unavoidable. Especially in the field of the discipline speed climbing, which will be performed as a single discipline at the Olympic Games 2024 in Paris, the current state of the art of movement analysis only consists of video analysis and the benefit of the experience of

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Ait-Bennacer, Fatima-Ezzahra, Abdessadek Aaroud, Khalid Akodadi, and Bouchaib Cherradi. "Applying Deep Learning and Computer Vision Techniques for an e-Sport and Smart Coaching System Using a Multiview Dataset: Case of Shotokan Karate." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 12 (2022): 35–53. http://dx.doi.org/10.3991/ijoe.v18i12.30893.

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Smart coaching and e-sport platforms have shown a great interest in the recent research studies. Through this study, we aim to globalize the practice of sport, especially Shotokan Karate, by connecting participants and coaches on an international scale through the integration of Artificial Intelligence techniques such as computer vision and deep learning, to give the possibility of carrying out national and international virtual training courses without logistical constraints. The proposed work aims to apply the latest action detection, action recognition, and action classification methods for

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Achmad Ivan Taruna Jaya, Pradini Puspitaningayu, Athaya Pradipa Adiwangsa, and Nobuo Funabiki. "Two-dimensional Human Pose Estimation using Key Points' Angular Detection for Basic Strength Training." Journal of Intelligent System and Telecommunication 1, no. 1 (2024): 105–19. https://doi.org/10.26740/jistel.v1n1.p105-119.

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Human pose estimation (HPE) has emerged as a crucial topic in computer vision, with applications ranging from sports training to injury prevention. This paper proposes a real-time 2D pose estimation system that leverages keypoint angle detection for basic strength exercises, such as squats, bicep curls, and deadlifts. The system integrates MediaPipe to detect joint positions and analyze them against optimal movement patterns determined by fitness guidelines. Primary data were collected using standard webcam recordings of exercises performed by expert trainers, enabling the system to establish

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T. Psota, Eric, Ty Schmidt, Benny Mote, and Lance C. Pérez. "Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification." Sensors 20, no. 13 (2020): 3670. http://dx.doi.org/10.3390/s20133670.

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Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification ne

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Liu, Yuhang, Miao Wang, Shuaibiao Hou, Xiao Wang, and Bing Shi. "Deep Learning-Based Markerless Hand Tracking for Freely Moving Non-Human Primates in Brain–Machine Interface Applications." Electronics 14, no. 5 (2025): 920. https://doi.org/10.3390/electronics14050920.

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The motor cortex of non-human primates plays a key role in brain–machine interface (BMI) research. In addition to recording cortical neural signals, accurately and efficiently capturing the hand movements of experimental animals under unconstrained conditions remains a key challenge. Addressing this challenge can deepen our understanding and application of BMI behavior from both theoretical and practical perspectives. To address this issue, we developed a deep learning framework that combines Yolov5 and RexNet-ECA to reliably detect the hand joint positions of freely moving primates at differe

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